Interference Cancellation Method, Medium and Electronic Device

Abstract

The present application relates to the technical field of signal processing, and discloses an interference cancellation method, medium and device. The method includes: an acquisition step of acquiring a plurality of imaging echoes and obtaining original k-space data according to the plurality of imaging echoes; a construction step of constructing a block Hankel matrix; a decomposition step of performing eigenvalue decomposition on the block Hankel matrix, and making the block Hankel matrix satisfy the low-rank constraint by means of rank truncation to obtain a low-rank matrix; a recovery step of performing recovery based on the low-rank matrix to obtain current k-space data that satisfies the low-rank constraint; an update step of generating updated k-space data according to the original k-space data and the current k-space data; a determination step of determining whether a predetermined condition is satisfied, and when the predetermined condition is not satisfied, returning to the construction step, and when the predetermined condition is satisfied, entering an image reconstruction step; an image reconstruction step of performing image reconstruction by using the updated k-space data. The present invention can eliminate the influence of magnetic field disturbance on magnetic resonance imaging.

Description

Technical Field

[0001] This application relates to the technical field of signal processing, and particularly to an interference cancellation method, medium and electronic device for magnetic resonance imaging. Background Art

[0002] Rail transit (subway, train, etc.) or high-power electrical appliances near this magnetic resonance imaging system can cause changes in the magnetic field strength and direction in the imaging area. When there is magnetic field disturbance, the magnetic resonance signals received by the receiving coil will change in phase accordingly, resulting in inconsistent data phases between different phase-encoding lines (k-space lines) in the entire k-space, and further leading to image artifacts. This phenomenon is particularly obvious in low-field or ultra-low-field systems with relatively low main magnetic field strength. In addition, in clinical applications, patient movement (such as breathing, swallowing movements, etc.) can also cause magnetic field disturbances.

[0003] In order to eliminate the influence of magnetic field disturbance, a fluxgate sensor can be used to detect the magnetic field disturbance and drive an active shielding coil to offset the influence of the disturbance, but this solution is costly. Summary of the Invention

[0004] Embodiments of this application provide an interference cancellation method, device, medium and equipment.

[0005] In a first aspect, embodiments of this application provide an interference cancellation method, including: an acquisition step of acquiring a plurality of imaging echoes, obtaining original k-space data based on the plurality of imaging echoes, and using the original k-space data as the k-space data for construction; a construction step of constructing a block Hankel matrix based on the k-space data for construction; a decomposition step of performing eigenvalue decomposition on the block Hankel matrix, and making the block Hankel matrix satisfy the low-rank constraint through rank truncation to obtain a low-rank matrix; a recovery step of performing recovery based on the low-rank matrix to obtain current k-space data that satisfies the low-rank constraint; an update step of generating updated k-space data according to the original k-space data and the current k-space data; a judgment step of judging whether a predetermined condition is satisfied. When the predetermined condition is not satisfied, using the updated k-space data as the k-space data for construction and returning to the construction step. When the predetermined condition is satisfied, entering an image reconstruction step; an image reconstruction step of performing image reconstruction using the updated k-space data.

[0006] In a possible implementation of the above first aspect, the predetermined condition is that the updated k-space data has converged or has reached a predetermined number of iterations.

[0007] In a possible implementation of the above first aspect, the original k-space data includes data of multiple k-space lines in the original k-space at respective multiple sampling points, and the current k-space data includes data of multiple k-space lines in the k-space satisfying the low-rank constraint at the respective multiple sampling points.

[0008] In a possible implementation of the above first aspect, the updating step includes: generating, according to the original k-space data and the current k-space data, an average phase difference between data of each k-space line at the multiple sampling points in the original k-space and data of the multiple sampling points in the k-space satisfying the low-rank constraint; performing filtering processing on the average phase differences of the multiple k-space lines; obtaining, based on the filtered average phase differences, an estimated phase change of each of the multiple k-space lines at the multiple sampling points; and generating, according to the estimated phase change, updated data of each of the multiple k-space lines at the multiple sampling points to obtain the updated k-space data.

[0009] In a possible implementation of the above first aspect, the phase difference ΔФ est (t) between data of each k-space line at the multiple sampling points in the original k-space and data of the multiple sampling points in the k-space satisfying the low-rank constraint is obtained according to the following formula 1

[0010] ΔФ est (t) = angle(S img (t) × conj(S LR (t))) Formula 1

[0011] where S img (t) is the data of the sampling point t of the nth k-space line in the original k-space, and S LR (t) is the data of the sampling point t of the nth k-space line in the k-space satisfying the low-rank constraint, and the sampling point t is relative to the moment when the radio frequency pulse is applied.

[0012] The weight w(t) of the phase difference ΔФ est (t) is obtained according to the following formula 2

[0013] w(t) = abs(S img (t) × conj(S LR (t))) Formula 2

[0014] The average phase difference Ф0 of each k-space line is obtained according to the following formula 3

[0015]

[0016] where TE is the echo time of each imaging echo.

[0017] In a possible implementation of the above first aspect, filtering is performed on the average phase difference Φ0 of each of the multiple k-space lines to obtain the filtered average phase difference Φ'0 of each k-space line.

[0018] And the phase change estimate ΔΦ‘ est (t) is obtained according to the following formula 4.

[0019]

[0020] In a possible implementation of the above first aspect, the updated data S' img (t) at the multiple sampling points of each k-space line is generated according to the following formula 5.

[0021]

[0022] In a possible implementation of the above first aspect, the multiple imaging echoes are respectively filled into the corresponding k-space lines of k-space, and each k-space line is sampled at multiple sampling points to obtain the original k-space data.

[0023] In a possible implementation of the above first aspect, the filtering process is weighted average filtering or Kalman filtering.

[0024] In a possible implementation of the above first aspect, the multiple imaging echoes are obtained using a gradient echo sequence, and the gradient echo sequence is composed of a radio frequency pulse, a slice selection gradient, a slice rephasing gradient, a phase encoding gradient, a pre-phase gradient, a readout gradient, a spoiling gradient, and a phase unwrapping gradient.

[0025] In a second aspect, an interference cancellation device is provided in an embodiment of the present application. The acquisition module acquires multiple imaging echoes, obtains original k-space data based on the multiple imaging echoes, and uses the original k-space data as the k-space data for construction; the construction module constructs a block Hankel matrix based on the k-space data for construction; the decomposition module performs eigenvalue decomposition on the block Hankel matrix, and makes the block Hankel matrix satisfy the low-rank constraint through rank truncation to obtain a low-rank matrix; the recovery module performs recovery based on the low-rank matrix to obtain the current k-space data that satisfies the low-rank constraint; the update module generates updated k-space data according to the original k-space data and the current k-space data; the judgment module determines whether a predetermined condition is satisfied. When the predetermined condition is not satisfied, the updated k-space data is used as the k-space data for construction and returned to the construction module. When the predetermined condition is satisfied, it enters the image reconstruction module; the image reconstruction module performs image reconstruction using the updated k-space data. For example, the above acquisition module, construction module, decomposition module, recovery module, update module, judgment module, and image reconstruction module can be implemented by a processor in an electronic device that has the functions of these modules or units.

[0026] In a third aspect, an embodiment of the present application provides a computer-readable storage medium, on which instructions are stored. When the instructions are executed on a computer, the computer is caused to execute the interference cancellation method in the first aspect above.

[0027] In a fourth aspect, an embodiment of the present application provides an electronic device, including: one or more processors; one or more memories; the one or more memories store one or more programs. When the one or more programs are executed by the one or more processors, the electronic device is caused to execute the interference cancellation method in the first aspect above. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 According to some embodiments of the present application, a schematic structural diagram of a magnetic resonance imaging device is shown;

[0029] Figure 2 According to some embodiments of the present application, a schematic flowchart of an interference cancellation method is shown;

[0030] Figure 3 According to some embodiments of the present application, a timing diagram of a gradient echo sequence is shown;

[0031] Figure 4 It shows a schematic diagram of the k-space line in the filtered k-space and the phase difference before and after filtering according to some embodiments;

[0032] Figure 5According to some embodiments of the present application, a block diagram of a computer of a magnetic resonance imaging device is shown. Detailed implementation manners

[0033] Illustrative embodiments of the present application include, but are not limited to, interference cancellation methods, devices, media, and apparatuses for magnetic resonance imaging.

[0034] The interference cancellation method provided by the embodiments of the present application can be applied to magnetic resonance imaging (MRI).

[0035] As an example, in a magnetic resonance imaging scenario, the electronic device can be a device with magnetic resonance imaging capabilities, which will be referred to as a magnetic resonance imaging device herein.

[0036] In the following embodiments, the interference cancellation method executed by the magnetic resonance imaging device in the magnetic resonance imaging scenario is mainly used as an example to illustrate the interference cancellation method provided by the embodiments of the present application. Similarly, the implementation details of the interference cancellation method executed by the electronic device in other application scenarios will not be elaborated one by one herein, and some descriptions can refer to the relevant descriptions of the interference cancellation method executed by the magnetic resonance imaging device.

[0037] Magnetic resonance imaging technology can generate medical images in medical or clinical application scenarios for disease diagnosis. Specifically, magnetic resonance imaging technology can use the signals generated by the resonance of atomic nuclei in a strong magnetic field for image reconstruction, and make tomographic images of cross-sections, sagittal planes, coronal planes, and various oblique planes of objects such as the human body.

[0038] In the implementation of the present application, the magnetic resonance imaging device can be a low-field or ultra-low-field magnetic resonance imaging device, or a mid-field or high-field magnetic resonance imaging device. As an example, magnetic resonance imaging systems in clinical applications can usually be classified into high-field (above 1T), mid-field (0.3 - 1T), low-field (0.1 - 0.3T), and ultra-low-field (below 0.1T) according to the magnetic field strength.

[0039] It can be understood that the embodiments of the present application are mainly applied to low-field or ultra-low-field magnetic resonance imaging devices to eliminate magnetic field disturbances during magnetic resonance imaging, thereby eliminating artifacts existing in magnetic resonance imaging and improving the quality of magnetic resonance imaging.

[0040] The embodiments of the present application will be further described in detail below with reference to the accompanying drawings.

[0041] As Figure 1As shown, it is a possible structural schematic diagram of a magnetic resonance imaging device provided by an embodiment of the present application. The magnetic resonance imaging device 100 may include: a computer 101, a spectrometer 102, a gradient amplifier 103, gradient coils 104, a transmitting radio frequency amplifier 105, a transmitting radio frequency coil (also referred to as a transmitting coil) 106, a receiving radio frequency amplifier 107, a receiving radio frequency coil 108 (also referred to as a receiving coil), and a magnet 109.

[0042] Specifically, the computer 101 is used to issue instructions to the spectrometer 102 under the control of an operator, so as to trigger the spectrometer 102 to generate the waveforms of the gradient signal and the radio frequency signal according to the instructions. After the gradient signal generated by the spectrometer 102 is amplified by the gradient amplifier 103, a magnetic field gradient is formed by the gradient coils 104, thereby realizing spatial gradient encoding for magnetic resonance signals (specifically magnetic resonance imaging signals). Specifically, spatial gradient encoding is used to spatially locate magnetic resonance signals, that is, to distinguish the positions where the magnetic resonance signals originate. The radio frequency signal generated by the spectrometer 102 is amplified by the transmitting radio frequency amplifier 105 and transmitted by the transmitting radio frequency coil 106, thereby exciting protons (hydrogen atomic nuclei) in the imaging region. Among them, the excited protons can emit radio frequency signals, which can be received by the receiving coil 108, amplified by the receiving radio frequency amplifier 107, and then converted into digital signals by the spectrometer 102, and further transmitted to the computer 101 for processing to obtain and display images. In addition, the magnet 109 can be any suitable type of magnet capable of generating a main magnetic field.

[0043] In some embodiments, the above-mentioned receiving coil 108 can be implemented using a single or multiple phased array coils widely used in modern medical magnetic resonance imaging.

[0044] It can be understood that in the embodiments of the present application, the design and layout (deployment position, deployment direction, etc.) of the receiving coil in the magnetic resonance imaging device 100 are not specifically limited, and any feasible solution can be adopted.

[0045] For gradient echo, the magnetic resonance signal received by the receiving coil can be expressed as

[0046]

[0047] where S(t) is the magnetic resonance imaging signal received by the receiving coil at time t, ρ is the proton spin distribution, γ is the gyromagnetic ratio, G is the field strength distribution at each location within the Field of View (FOV), and x represents the spatial vector coordinate. If a subway or the like passes nearby, then a magnetic field perturbation will be generated, and it can be considered that this perturbation is a constant within the entire field of view, that is

[0048] G' x = G c + ΔG Formula (2)

[0049] Then, Formula (1) can be rewritten as

[0050]

[0051] That is

[0052]

[0053] Therefore, the phase of the magnetic resonance signal will change, and the phase change can be expressed as

[0054] ΔФ(t) = -γΔGt Formula (5)

[0055] Based on the above description, the main working process of the magnetic field perturbation interference cancellation method executed by the nuclear magnetic resonance imaging device 100 will be specifically introduced below. Specifically, the technical details described in the above-mentioned nuclear magnetic resonance imaging device 100 still apply in the following method process. To avoid repetition, some will not be elaborated again. In some embodiments, the execution subject of the magnetic field perturbation interference cancellation method of the present application can be the nuclear magnetic resonance imaging device 100, specifically the computer 101 in the nuclear magnetic resonance imaging device 100. As Figure 1 shown, it is a schematic flow chart of an interference cancellation method provided by the present application Figure 2 and shows the timing diagram of the gradient echo sequence. In this embodiment, this method is used to eliminate the influence of magnetic field perturbation on magnetic resonance imaging Figure 3 Acquisition step 201: The nuclear magnetic resonance imaging device 100 acquires a plurality of imaging echoes, and based on the plurality of imaging echoes, obtains the original k-space data, and uses the original k-space data as the k-space data for construction

[0056] Specifically, the magnetic resonance imaging signals received by the receiving coil 108 are acquired to obtain a plurality of imaging echoes. Refer to

[0057] , before the acquisition, the nuclear magnetic resonance imaging device 100 applies a radio frequency pulse, and at the same time applies a slice selection gradient, then applies a slice selection rephasing gradient, and at the same time applies a phase encoding gradient and a pre-phase gradient in the readout direction. Then, the nuclear magnetic resonance imaging device 100 acquires the magnetic resonance imaging signal, and at the same time applies a readout gradient to obtain an imaging echo. After acquiring an imaging echo, the nuclear magnetic resonance imaging device 100 applies a phase rewinder gradient and a spoiler gradient at the same time to eliminate the influence of the remaining transverse magnetization vector on the next acquisition of the imaging echo Figure 3 , in the acquisition, the nuclear magnetic resonance imaging device 100 applies a radio frequency pulse, and at the same time applies a slice selection gradient, then applies a slice selection rephasing gradient, and at the same time applies a phase encoding gradient and a pre-phase gradient in the readout direction. Then, the nuclear magnetic resonance imaging device 100 acquires the magnetic resonance imaging signal, and at the same time applies a readout gradient to obtain an imaging echo. After acquiring an imaging echo, the nuclear magnetic resonance imaging device 100 applies a phase rewinder gradient and a spoiler gradient at the same time to eliminate the influence of the remaining transverse magnetization vector on the next acquisition of the imaging echo

[0058] The magnetic resonance imaging device 100 fills an unfilled k-space line in the k-space with one acquired imaging echo, and then repeats the acquisition process until all the k-space lines in the k-space are filled with the corresponding imaging echoes, thus obtaining a plurality of imaging echoes.

[0059] It can be understood that the plurality of imaging echoes are filled into the corresponding k-space lines in the k-space respectively, and each k-space line is sampled at a plurality of sampling points, thereby obtaining the original k-space data.

[0060] It can be understood that the original k-space data includes the data of each of the multiple k-space lines in the original k-space at a plurality of sampling points.

[0061] It can be understood that a plurality of imaging echoes are obtained by using a gradient echo sequence, and the gradient echo sequence is composed of a radio frequency pulse, a slice selection gradient, a slice rephasing gradient, a phase encoding gradient, a pre-phase gradient, a readout gradient, a spoiling gradient, and a phase unwrapping gradient.

[0062] In the construction step 202, the magnetic resonance imaging device 100 constructs a block Hankel matrix based on the k-space data for construction. It can be understood that the k-space data for construction at this time is the original k-space data.

[0063] In the decomposition step 203, the magnetic resonance imaging device 100 performs eigenvalue decomposition on the block Hankel matrix, and makes the block Hankel matrix satisfy the low-rank constraint by means of rank truncation, so as to obtain a low-rank matrix. It can be understood that the low-rank matrix is approximate to the block Hankel matrix.

[0064] In the recovery step 204, the magnetic resonance imaging device 100 performs recovery based on the low-rank matrix to obtain the current k-space data that satisfies the low-rank constraint. It can be understood that the current k-space data includes the data of each of the multiple k-space lines in the k-space that satisfies the low-rank constraint at a plurality of sampling points.

[0065] In the update step, the magnetic resonance imaging device 100 generates updated k-space data according to the original k-space data and the current k-space data.

[0066] The update step includes generating the average phase difference between the data of each k-space line at a plurality of sampling points in the original k-space and the data of each k-space line at a plurality of sampling points in the k-space that satisfies the low-rank constraint according to the original k-space data and the current k-space data.

[0067] Specifically, the phase difference ΔФ est (t)

[0068] ΔФ est(t) = angle(S img (t) × conj(S LR (t))) Equation 1

[0069] where S img (t) is the data of the sampling point t of the nth k-space line in the original k-space, and S LR (t) is the data of the sampling point t of the nth k-space line in the k-space satisfying the low-rank constraint. The sampling point t is relative to the moment when the radio frequency pulse is applied. angle is a function for calculating the phase angle, and conj is a function for calculating the conjugate complex number. It can be understood that with the moment when the radio frequency pulse is applied as the 0 moment, the sampling point t is the moment relative to the 0 moment.

[0070] The phase difference ΔФ is obtained according to the following Equation 2 est with the weight w(t) of S

[0071] w(t) = abs(S img (t) × conj(S LR (t))) Equation 2

[0072] where abs is a function for calculating the amplitude.

[0073] The average phase difference Ф0 of each k-space line is obtained according to the following Equation 3

[0074]

[0075] where TE is the echo time of each imaging echo.

[0076] In this way, the average phase difference Ф0 of each k-space line is obtained. Then, other prior information can be introduced to reduce the influence of noise on the estimation accuracy of the phase difference. For example, it can be considered that the magnetic field perturbation is low-frequency. Then, according to the acquisition time sequence, the average phase differences Ф0 of multiple k-space lines are rearranged respectively and filtered.

[0077] Specifically, the average phase differences Ф0 of multiple k-space lines are arranged according to the acquisition time of each imaging echo and filtered as a time series, so as to obtain the filtered average phase difference Φ’0 of each k-space line.

[0078] In the embodiment of the present invention, the filtering process is a weighted average filtering process, a Kalman filter, or other filtering processes without limitation.

[0079] The weighted average filtering can be expressed as:

[0080]

[0081] where ΔФi The phase change corresponding to the i-th k-space line sorted by acquisition time, ΔФ' n The estimation of the phase change corresponding to the n-th k-space line after filtering, w i Is the weight when performing weighted average filtering for the i-th spatial line; here the window width of the weighted average filtering is 2m + 1. Generally speaking, the closer i is to n, the greater the weight. For example, the weight can be defined according to the Gaussian function:

[0082]

[0083] The weighted average filtering that defines the weight according to this Gaussian function is Gaussian filtering.

[0084] Figure 4 Is a schematic diagram showing the k-space line in the k-space after filtering processing and the phase difference before and after filtering. Among them, the thin solid line is the estimation of the phase difference before filtering, and the thick dashed line is the estimation of the phase difference after filtering. It can be seen that through the above filtering processing, the influence of noise can be reduced.

[0085] Next, based on the average value of the phase difference after filtering processing, the estimation of the phase change of each of multiple k-space lines at multiple sampling points is obtained.

[0086] Specifically, the estimation of the phase change ΔΦ‘ of each of multiple k-space lines at multiple sampling points is obtained according to the following formula 4 est (t),

[0087]

[0088] Next, according to the phase change estimation, update data of each of multiple k-space lines at multiple sampling points is generated to obtain updated k-space data.

[0089] Specifically, the updated data S' of each k-space line at multiple sampling points is generated according to the following formula 5 img (t),

[0090]

[0091] It can be understood that the updated k-space data includes the updated data S' of each of multiple k-space lines at multiple sampling points img (t). Wherein, i represents an imaginary number.

[0092] In the judgment step 206, the magnetic resonance imaging device 100 judges whether a predetermined condition is satisfied. When the predetermined condition is not satisfied, the updated k-space data is used as the k-space data for construction and returns to the construction step 202. When the predetermined condition is satisfied, it enters the image reconstruction step 207.

[0093] Among them, the predetermined condition may be that a predetermined number of iterations has been reached or the updated k-space data has converged. It can be understood that the predetermined condition can also be other suitable conditions without any limitation.

[0094] It can be understood that, for example, if the predetermined number of iterations (repetitions) is 10 times, and it is determined that the current number of iterations has not reached 10 times, the updated k-space data obtained above is used as the k-space data for construction, and the construction step 202 is returned, and the above steps 202-206 are repeated. If it is determined that the current number of iterations has reached 10 times, the iterative process is stopped and the image reconstruction step 207 is entered.

[0095] It can be understood that if it is determined that the updated k-space data has not converged, the updated k-space data obtained above is used as the k-space data for construction, and the construction step 202 is returned, and the above steps 202-206 are repeated. If it is determined that the updated k-space data has converged, the image reconstruction step 207 is entered.

[0096] In the image reconstruction step 207, the magnetic resonance imaging device 100 uses the updated k-space data for image reconstruction.

[0097] It can be understood that the present invention can use a Hankel matrix to obtain a low-rank matrix for correcting the phase change of the k-space lines in the k-space, thereby eliminating the influence of magnetic field disturbance on magnetic resonance imaging, eliminating the artifacts existing in magnetic resonance imaging, and improving the quality of magnetic resonance imaging.

[0098] In addition, as can be seen from the above formula (5), the longer the echo time, the more significant the phase change caused by the magnetic field disturbance. Therefore, the phase change caused by the magnetic field disturbance can also be minimized by reducing TE.

[0099] If the magnetic resonance imaging system is in a non-electromagnetic shielding environment, it is necessary to first remove the influence of electromagnetic interference (refer to the previously filed patents "Interference Elimination Method, Medium and Device", CN113176528A / CN113180636A / CN113203969A, which can be incorporated herein by reference), and then use the method proposed by the present invention to eliminate the influence of magnetic field disturbance.

[0100] Similarly, for other scenarios to which the embodiments of the present application are applied, the electronic device can also implement the interference elimination method according to steps similar to the above steps 201-207, except that the execution subject is different.

[0101] Now refer to Figure 5 , which shows a block diagram of a computer in the magnetic resonance imaging device 100 according to an embodiment of the present application. Figure 5FIG. 1400 schematically illustrates an example computer in accordance with multiple embodiments. In one embodiment, system 1400 may include one or more processors 1404, system control logic 1408 coupled to at least one of processors 1404, system memory 1412 coupled to system control logic 1408, non-volatile memory (NVM) 1416 coupled to system control logic 1408, and network interface 1420 coupled to system control logic 1408.

[0102] In some embodiments, processor 1404 may include one or more single-core or multi-core processors. In some embodiments, processor 1404 may include any combination of general-purpose processors and dedicated processors (e.g., graphics processors, application processors, baseband processors, etc.). In embodiments where system 1400 employs eNB (Evolved Node B) 101 or RAN (Radio Access Network) controller 102, processor 1404 may be configured to execute various conforming embodiments, e.g., as Figure 2 shown in the embodiments.

[0103] In some embodiments, system control logic 1408 may include any suitable interface controller to provide any suitable interface to at least one of processors 1404 and / or any suitable device or component communicating with system control logic 1408.

[0104] In some embodiments, system control logic 1408 may include one or more memory controllers to provide an interface to system memory 1412. System memory 1412 may be used to load and store data and / or instructions. In some embodiments, memory 1412 of system 1400 may include any suitable volatile memory, such as suitable dynamic random access memory (DRAM).

[0105] NVM / memory 1416 may include one or more tangible, non-transitory computer-readable media for storing data and / or instructions. In some embodiments, NVM / memory 1416 may include any suitable non-volatile memory such as flash memory and / or any suitable non-volatile storage device, such as at least one of HDD (Hard Disk Drive), CD (Compact Disc) drive, DVD (Digital Versatile Disc) drive.

[0106] The NVM / memory 1416 may include a portion of the storage resources on the device where the system 1400 is installed, or it may be accessible by the device but not necessarily part of the device. For example, the NVM / memory 1416 can be accessed via the network interface 1420 over a network.

[0107] In particular, the system memory 1412 and the NVM / memory 1416 may respectively include: a temporary copy and a permanent copy of the instructions 1424. The instructions 1424 may include: instructions that, when executed by at least one of the processors 1404, cause the computer 1400 to implement the method as Figure 2 shown. In some embodiments, the instructions 1424, hardware, firmware, and / or its software components may additionally / alternatively be placed in the system control logic 1408, the network interface 1420, and / or the processor 1404.

[0108] The network interface 1420 may include a transceiver for providing a radio interface for the system 1400, and thus communicating with any other suitable devices (such as front-end modules, antennas, etc.) over one or more networks. In some embodiments, the network interface 1420 may be integrated with other components of the system 1400. For example, the network interface 1420 may be integrated with at least one of the processor 1404, the system memory 1412, the NVM / memory 1416, and a firmware device (not shown) having instructions, and when at least one of the processors 1404 executes the instructions, the computer 1400 implements the method as Figure 2 shown.

[0109] The network interface 1420 may further include any suitable hardware and / or firmware to provide a multiple-input multiple-output radio interface. For example, the network interface 1420 may be a network adapter, a wireless network adapter, a telephone modem, and / or a wireless modem.

[0110] In one embodiment, at least one of the processors 1404 may be logically packaged with one or more controllers for the system control logic 1408 to form a system-in-package (SiP). In one embodiment, at least one of the processors 1404 may be integrated with the logic of one or more controllers for the system control logic 1408 on the same die to form a system-on-chip (SoC).

[0111] The computer 1400 may further include: an input / output (I / O) device 1432. The I / O device 1432 may include a user interface that enables a user to interact with the computer 1400; the design of the peripheral component interface enables peripheral components to also interact with the computer 1400. In some embodiments, the computer 1400 further includes sensors for determining at least one of environmental conditions and location information related to the computer 1400.

[0112] In some embodiments, the user interface may include, but is not limited to, a display (e.g., a liquid crystal display, a touch screen display, etc.), a speaker, a microphone, one or more cameras (e.g., a still image camera and / or a video camera), a flashlight (e.g., a light emitting diode flash), and a keyboard. For example, the above user interface can be used to display imaging images of a magnetic resonance imaging process and images of k-space, etc.

[0113] Embodiments of the mechanisms disclosed in this application can be implemented in hardware, software, firmware, or a combination of these implementation methods. Embodiments of this application can be implemented as a computer program or program code executed on a programmable system, which includes at least one processor, a storage system (including volatile and non-volatile memories and / or storage elements), at least one input device, and at least one output device.

[0114] The program code can be applied to the input instructions to perform the various functions described in this application and generate output information. The output information can be applied to one or more output devices in a known manner. For the purposes of this application, a processing system includes any system having a processor such as, for example, a digital signal processor (DSP), a microcontroller, an application specific integrated circuit (ASIC), or a microprocessor.

[0115] The program code can be implemented in a high-level procedural language or an object-oriented programming language to communicate with the processing system. When needed, the program code can also be implemented in assembly language or machine language. In fact, the mechanisms described in this application are not limited to the scope of any specific programming language. In any case, the language can be a compiled language or an interpreted language.

[0116] In some cases, the disclosed embodiments may be implemented in hardware, firmware, software, or any combination thereof. The disclosed embodiments may also be implemented as instructions carried or stored on one or more transitory or non-transitory machine-readable (e.g., computer-readable) storage media, which may be read and executed by one or more processors. For example, the instructions may be distributed via a network or via other computer-readable media. Thus, machine-readable media may include any mechanism for storing or transmitting information in a form readable by a machine (e.g., a computer), including but not limited to, floppy disks, optical disks, optical discs, compact disc read-only memory (CD-ROM), magneto-optical disks, read-only memory (ROM), random access memory (RAM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), magnetic or optical cards, flash memory, or tangible machine-readable memories for transmitting information (e.g., carrier waves, infrared signals, digital signals, etc.) in electrical, optical, acoustic, or other forms via the Internet. Thus, machine-readable media includes any type of machine-readable media suitable for storing or transmitting electronic instructions or information in a form readable by a machine (e.g., a computer).

[0117] In the drawings, some structural or method features may be shown in a particular arrangement and / or order. However, it should be understood that such a particular arrangement and / or ordering may not be required. Rather, in some embodiments, these features may be arranged in a manner and / or order different from that shown in the illustrative drawings. Additionally, the inclusion of a structural or method feature in a particular figure does not imply that such a feature is required in all embodiments, and in some embodiments, these features may not be included or may be combined with other features.

[0118] It should be noted that each unit / module mentioned in the device embodiments of this application is a logical unit / module. Physically, a logical unit / module may be a physical unit / module, may be a part of a physical unit / module, or may also be implemented as a combination of multiple physical units / module. The physical implementation manner of these logical units / module themselves is not the most important. The combination of the functions implemented by these logical units / module is the key to solving the technical problems proposed in this application. In addition, in order to highlight the innovative part of this application, the above-mentioned device embodiments of this application do not introduce units / modules that are not closely related to solving the technical problems proposed in this application, which does not mean that there are no other units / modules in the above-mentioned device embodiments.

[0119] It should be noted that in the examples and the description of this patent, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, such that a process, method, article or device comprising a series of elements not only includes those elements but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising one" does not exclude the presence of additional identical elements in the process, method, article or device comprising said element.

[0120] Although this application has been illustrated and described by reference to certain preferred embodiments thereof, those of ordinary skill in the art should understand that various changes may be made therein in form and detail without departing from the spirit and scope of this application.

Claims

1. A method for interference cancellation, characterized in that, The method includes: A collection step of collecting a plurality of imaging echoes, obtaining original k-space data based on the plurality of imaging echoes, and using the original k-space data as the k-space data for construction; A construction step of constructing a block Hankel matrix based on the k-space data for construction; A decomposition step of performing eigenvalue decomposition on the block Hankel matrix, and making the block Hankel matrix satisfy the low-rank constraint by means of rank truncation to obtain a low-rank matrix; A recovery step of performing recovery based on the low-rank matrix to obtain current k-space data that satisfies the low-rank constraint; An update step of generating updated k-space data according to the original k-space data and the current k-space data; A judgment step of judging whether a predetermined condition is satisfied. When the predetermined condition is not satisfied, the updated k-space data is used as the k-space data for construction, and the construction step is returned. When the predetermined condition is satisfied, an image reconstruction step is entered; An image reconstruction step of performing image reconstruction using the updated k-space data, The update step includes: Generating an average phase difference between data of multiple sampling points of each k-space line in the original k-space and data of the multiple sampling points in the k-space that satisfies the low-rank constraint according to the original k-space data and the current k-space data; Performing filtering processing on the average phase difference of each of the multiple k-space lines; Based on the filtered average phase difference, obtaining an estimation of the phase change of each of the multiple k-space lines at the multiple sampling points; Generating updated data of each of the multiple k-space lines at the multiple sampling points according to the phase change estimation to obtain the updated k-space data, Among them, the phase difference ΔФ between the multiple sampling points of each k-space line in the original k-space and the multiple sampling points in the k-space satisfying the low-rank constraint is obtained according to the following formula 1 est (t), ΔФ est (t) = angle(S img (t) × conj(S LR (t))) Equation 1 where S img (t) is the data of the sampling point t of the n-th k-space line in the original k-space, and S LR (t) is the data of the sampling point t of the n-th k-space line in the k-space that satisfies the low-rank constraint, and the sampling point t is relative to the moment when the radio frequency pulse is applied The phase difference ΔФ is obtained according to the following formula 2 est The weight w(t) of (t), w(t) = abs(S img (t) × conj(S LR (t))) Equation 2 Obtaining the average phase difference Ф0 of each k-space line according to the following formula 3, where TE is the echo time of each imaging echo.

2. The method according to claim 1, wherein The predetermined condition is that a predetermined number of iterations has been reached or the updated k-space data has converged.

3. The method according to claim 2, wherein The original k-space data includes data of multiple sampling points of each of multiple k-space lines in the original k-space, and the current k-space data includes data of multiple sampling points of each of multiple k-space lines in the k-space that satisfies the low-rank constraint.

4. The method according to claim 1, characterized in that, Performing filtering processing on the average phase difference Φ0 of each of the multiple k-space lines to obtain a filtered average phase difference Φ’0 of each k-space line; and obtain the estimated phase change ΔΦ' according to the following formula 4 est (t), 5. The method according to claim 4, wherein Generate the updated data S′ of each k-space line at the multiple sampling points according to the following formula 5 img (t), 6. The method according to any one of claims 1 to 5, characterized in that Filling each of the multiple imaging echoes into the corresponding k-space line of the k-space, and sampling each k-space line at multiple sampling points to obtain the original k-space data.

7. The method according to claim 6, wherein The filtering processing is weighted average filtering processing or Kalman filtering.

8. The method according to claim 1, characterized in that, The multiple imaging echoes are obtained using a gradient echo sequence, and the gradient echo sequence is composed of a radio frequency pulse, a slice selection gradient, a slice rephasing gradient, a phase encoding gradient, a pre-phase gradient, a readout gradient, a spoiling gradient, and a phase rewinder gradient.

9. A computer-readable storage medium, characterized in that, Instructions are stored on the storage medium, and when the instructions are executed on a computer, the computer executes the interference cancellation method according to any one of claims 1 to 8.

10. An electronic device, characterized in that, Including: One or more processors; One or more memories; the one or more memories store one or more programs, which, when executed by the one or more processors, cause the electronic device to perform the interference cancellation method according to any one of claims 1 to 8.

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